Lstm Price Movement Prediction for Stock Market - Agrawal, Sugam; Bhadauriya, Saurabh; Narayan, Vipul (Journal of Neonatal Surgery, 2025)
The prediction of stock market prices has historically posed significant challenges due to the difficult, chaotic, and wide range of characteristics of finance markets. Conventional models frequently fail to adequately capture the intricate patterns required for precise forecasting. This paper investigates the application of machine learning techniques, with a particular emphasis on Recurrent Neural Networks (RNN) and advanced variants, Long Short-Term Memory (LSTM), for the purpose of predicting future stock prices. LSTM models are specifically engineered to overcome the limitations associated with RNNs, particularly in managing long-term dependencies and addressing issues such as vanishing gradients, thereby rendering them particularly effective for the forecasting of time-series. The research centers on the development of an LSTM-based model aimed at predicting stock price fluctuations utilizing historical prices data alongside technical analysis indicators. Various experiments are conducted to evaluate the model's performance across various metrics, focusing on its predictive accuracy and the influence of different training epochs on model optimization. The findings indicate that the LSTM model substantially enhances prediction accuracy in comparison to other machine learning methodologies and traditional investment strategies. This study underscores the potential of sophisticated neural network architectures in yielding more dependable predictions within the inherently volatile realm of stock market forecasting